RWKV-Runner/backend-python/rwkv_pip/beta/cuda/att_one.cu
2023-09-15 16:30:11 +08:00

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#include "ATen/ATen.h"
#include <cuda_fp16.h>
#include <cuda_runtime.h>
#include <torch/extension.h>
#include "element_wise.h"
#include "util.h"
// Equivalent Python code:
// ww = t_first + k
// p = torch.maximum(pp, ww)
// e1 = torch.exp(pp - p)
// e2 = torch.exp(ww - p)
// wkv = ((e1 * aa + e2 * v) / (e1 * bb + e2)).to(dtype=x.dtype)
// ww = t_decay + pp
// p = torch.maximum(ww, k)
// e1 = torch.exp(ww - p)
// e2 = torch.exp(k - p)
// t1 = e1 * aa + e2 * v
// t2 = e1 * bb + e2
// r = r * wkv
// return t1, t2, p, r
struct WkvForwardOne {
const float *t_first;
const float *k;
const float *pp;
const float *aa;
const float *bb;
const float *t_decay;
const float *v;
/* out */ float *t1;
/* out */ float *t2;
/* out */ float *p;
/* in & out */ half *r;
__device__ void operator()(int i) const {
float ww = t_first[i] + k[i];
float pp_ = pp[i];
float p_ = (pp_ > ww) ? pp_ : ww;
float e1 = expf(pp_ - p_);
float e2 = expf(ww - p_);
float aa_ = aa[i];
float bb_ = bb[i];
float v_ = v[i];
r[i] = __hmul(r[i], __float2half(((e1 * aa_ + e2 * v_) / (e1 * bb_ + e2))));
ww = t_decay[i] + pp_;
float k_ = k[i];
p_ = (ww > k_) ? ww : k_;
e1 = expf(ww - p_);
e2 = expf(k_ - p_);
t1[i] = e1 * aa_ + e2 * v_;
t2[i] = e1 * bb_ + e2;
p[i] = p_;
}
};
/*
Equivalent Python code:
kx = xx * k_mix + sx * (1 - k_mix)
vx = xx * v_mix + sx * (1 - v_mix)
rx = xx * r_mix + sx * (1 - r_mix)
*/
struct Mix {
const half *xx;
const half *sx;
const half *k_mix;
const half *v_mix;
const half *r_mix;
/* out */ half *kx;
/* out */ half *vx;
/* out */ half *rx;
__device__ void operator()(int i) const {
half xx_ = xx[i];
half sx_ = sx[i];
half k_mix_ = k_mix[i];
half v_mix_ = v_mix[i];
half r_mix_ = r_mix[i];
kx[i] = __hadd(__hmul(xx_, k_mix_),
__hmul(sx_, __hsub(__float2half(1), k_mix_)));
vx[i] = __hadd(__hmul(xx_, v_mix_),
__hmul(sx_, __hsub(__float2half(1), v_mix_)));
rx[i] = __hadd(__hmul(xx_, r_mix_),
__hmul(sx_, __hsub(__float2half(1), r_mix_)));
}
};
using torch::Tensor;
void gemm_fp16_cublas_tensor(Tensor a, Tensor b, Tensor c);
Tensor att_one(Tensor x, Tensor ln_w, Tensor ln_b, Tensor sx, Tensor k_mix,
Tensor v_mix, Tensor r_mix, Tensor kw,
/* imm */ Tensor kx, Tensor vw, /* imm */ Tensor vx, Tensor rw,
/* imm */ Tensor rx, Tensor ow, Tensor t_first,
/* imm */ Tensor k, Tensor pp, Tensor ww, Tensor aa, Tensor bb,
Tensor t_decay, /* imm */ Tensor v, /* in & out */ Tensor r,
/* out */ Tensor x_plus_out, /* out */ Tensor t1,
/* out */ Tensor t2, /* out */ Tensor p) {
Tensor xx = at::layer_norm(x, {x.size(-1)}, ln_w, ln_b);
element_wise(Mix{data_ptr<half>(xx), data_ptr<half>(sx),
data_ptr<half>(k_mix), data_ptr<half>(v_mix),
data_ptr<half>(r_mix), data_ptr<half>(kx),
data_ptr<half>(vx), data_ptr<half>(rx)},
x.numel());
gemm_fp16_cublas_tensor(kx, kw, k);
gemm_fp16_cublas_tensor(vx, vw, v);
gemm_fp16_cublas_tensor(rx, rw, r);
at::sigmoid_(r);
element_wise(WkvForwardOne{data_ptr<float>(t_first), data_ptr<float>(k),
data_ptr<float>(pp), data_ptr<float>(aa),
data_ptr<float>(bb), data_ptr<float>(t_decay),
data_ptr<float>(v), data_ptr<float>(t1),
data_ptr<float>(t2), data_ptr<float>(p),
data_ptr<half>(r)},
x.numel());
gemm_fp16_cublas_tensor(r, ow, x_plus_out);
x_plus_out += x;
return xx;
}